The advent of Salesforce Marketing Cloud and Adobe Marketing Cloud demonstrates the need for enterprises to develop new ways of harnessing the vast potential of big data. Yet these marketing clouds beg the question of who will help marketers, the frontline of businesses, maximize marketing spending and ROI and help their brands win in the end. Simply moving software from onsite to hosted servers does not change the capabilities marketers require — real competitive advantage stems from intelligent use of big data.

Marc Benioff, who is famous for declaring that “Software Is Dead,” may face a similar fate with his recent bets on Buddy Media and Radian6. These applications provide data to people who must then analyze, prioritize and act — often at a pace much slower than the digital world. Data, content and platform insights are too massive for mere mortals to handle without costing a fortune. Solutions that leverage big data are poised to win — freeing up people to do the strategy and content creation that is best done by humans, not machines.

Big data is too big for humans to work with, at least in the all-important analytical construct of responding to opportunities in real time — formulating efficient and timely responses to opportunities generated from your marketing cloud, or pursuing the never-ending quest for perfecting search engine optimization (SEO) and search engine marketing (SEM). The volume, velocity and veracity of raw, unstructured data is overwhelming. Big data pioneers such as Facebook and eBay have moved to massive Hadoop clusters to process their petabytes of information.

In recent years, we’ve gone from analyzing megabytes of data to working with gigabytes, and then terabytes, and then petabytes and exabytes, and beyond. Two years ago, James Rogers, writing in The Street, wrote: “It’s estimated that 1 Petabyte is equal to 20 million four-door filing cabinets full of text.” We’ve become jaded to seeing such figures. But 20 million filing cabinets? If those filing cabinets were a standard 15 inches wide, you could line them up, side by side, all the way from Seattle to New York — and back again. One would need a lot of coffee to peruse so much information, one cabinet at a time. And, a lot of marketing staff.

Of course, we have computers that do the perusing for us, but as big data gets bigger, and as analysts, marketers and others seek to do more with the massive intelligence that can be pulled from big data, we risk running into a human bottleneck. Just how much can one person — or a cubicle farm of persons — accomplish in a timely manner from the dashboard of their marketing cloud? While marketing clouds do a fine job of gathering data, it still comes down to expecting analysts and marketers to interpret and act on it — often with data that has gone out of date by the time they work with it.

Hence, big data solutions leveraging machine learning, language models and prediction, in the form of self-learning solutions that go from using algorithms for harvesting information from big data, to using algorithms to initiate actions based on the data.

Yes, this may sound a bit frightful: Removing the human from the loop. Marketers indeed need to automate some decision-making. But the human touch will still be there, doing what only people can do — creating great content that evokes emotions from consumers — and then monitoring and fine-tuning the overall performance of a system designed to take actions on the basis of big data.

The big data revolution is just beginning as it moves beyond analytics. If we were building CRM again, we wouldn’t just track sales-force productivity; we’d recommend how you’re doing versus your competitors based on data across the industry. If we were building marketing automation software, we wouldn’t just capture and nurture leads generated by our clients, we’d find and attract more leads for them from across the Web. If we were building a financial application, it wouldn’t just track the financials of your company, it would compare them to public filings in your category so you could benchmark yourself and act on best practices.

Benioff is correct that there’s an undeniable trend that most marketing budgets today are betting more on social and mobile. The ability to manage social, mobile and Web analysis for better marketing has quickly become a real focus — and a big data marketing cloud is needed to do it. However, the real value and ROI comes from the ability to turn big data analysis into action, automatically. There’s clearly big value in big data, but it’s not cost-effective for any company to interpret and act on it before the trend changes or is over. Some reports find that 70 percent of marketers are concerned with making sense of the data and more than 91 percent are concerned with extracting marketing ROI from it. Incorporating big data technologies that create action means that your organization’s marketing can get smarter even while you sleep.

Raj De Datta founded BloomReach with 10 years of enterprise and entrepreneurial experience behind him. Most recently, he was an Entrepreneur-In-Residence at Mohr-Davidow Ventures. Previously, he was a Director of Product Marketing at Cisco. Raj also worked in technology investment banking at Lazard Freres. He holds a BSE in Electrical Engineering from Princeton and an MBA from Harvard Business School.

Editor’s Note: TechCrunch columnist Semil Shah currently works at Votizen and is based in Palo Alto. You can follow him on Twitter @semil

“In the Studio” opens its doors this week to one of Silicon Valley’s most quietly active venture capitalists who, after years working in technology operations for major networking companies, a stint with an Asian telecom giant, and nearly a decade investing in mobile, gaming, digital media, and networking companies, is paying particular attention to the implications of big data and the potential opportunities they create.

For the past decade, Ping Li has been investing in across a broad range of technology companies with Accel Partners, where he is a general partner. Since their defining Series A investment in Facebook, the firm has been on a roll, opening offices in New York City and expanding its footprint overseas, all while maintaining their anchor in the middle of Palo Alto’s University Avenue. And, over the past few years, Accel has also developed an interest in “big data.”

The term “big data” is thrown around often in conversation or at tech conferences, but despite the generalizations and hype, significant opportunities exist for entrepreneurs and investors alike. Last year, I attempted to analyze how big data impacted the consumer web and concluded that while opportunities were abundant, very few were in a positions to capitalize on them given the scarcity of talent in these specific areas of the consumer web.

Li and his partners at Accel are certainly looking at big data as it applies to consumer products — the massive amounts of unstructured social data we are all generating through social media and applications, waiting to be harvested. On the enterprise side of things, however, Li believes big data is on the verge of going mainstream, where datasets and analytical tools will soon be available to everyone, igniting new waves of innovation that could disrupt major public companies from the platform all the way to the application layer.

In this conversation, Li shares his views on the big data landscape and also offers subtle advice to potential founders looking into the space. Having the benefit to see many big data technologies and applications over the past few years, he has developed a keen sense of what minefields founders need to look out for when creating these technologies. To take things a step further, Li and his partners at Accel launched a $100M Big Data Fund, invested in creating an ecosystem of academics, technologists, and thought-leaders, and are hosting a private conference at Stanford on May 9 on this topic (technologists working on big data who would like to attend can contact Accel directly through the conference site).